The use of semiconductor technology has, over the last few decades, revolutionized the use of electrical and electronic goods. In particular, the increased use of semiconductor technology has resulted from an unappeasable need by business (as well as individuals) for better, smaller, faster and more reliable electronic goods.
The semiconductor wafer manufacturers have therefore needed to make commensurate improvements in product quality, as well as in the speed, quality and reliability of the semiconductor wafer manufacturing process. Clearly, in the mass-manufacture of semiconductor wafers, the manufacturer needs to minimize the number of faulty semiconductor wafers that are manufactured. Furthermore, the manufacturer clearly needs to recognize, as early as possible in the manufacturing process, when faulty semiconductor wafers are being manufactured, so that the manufacturing process can be checked and, if appropriate, corrected.
By continuously inspecting semiconductor wafers throughout the manufacturing process, flawed wafers may be removed and, if appropriate, the wafer or wafer manufacturing process corrected at any of the various manufacturing stages. This is much more preferable than completing the manufacture of a whole batch of wafers, only to find that a fault existed in the wafer manufacturing process, thereby creating a number of defective wafers or by failure of the wafer during use.
Wafer inspection is primarily performed by human visual inspection of manufactured wafers. A known mechanism for performing such an inspection is in the use of a Defect Source Identifier (DSI)™ system, developed by Applied Materials™ Inc. Wafers that exhibit a large number of defects are selected and pictures of the wafers taken. Each pixel/dot on the picture indicates an identified defect. The picture is then passed to the DSI system.
The DSI system requires an Operator to manually review this smart image (picture) of a manufactured wafer and classify it based on its pixel arrangement. An Operator would typically classify a defective wafer by looking at the defective pixel arrangement, for example looking to see if the defective pixels formed a substantially ‘star’ shaped figure or a ‘scratch’ shape. A defective wafer 100 is shown in FIG. 1. In this regard, the Operator may classify the defective wafer as one that leaves a bird's footprint impression 120.
Hundreds of previously selected and stored cases/wafer pictures are contained in a defect knowledge library (DKL), operably coupled to the DSI system. In this manner, an Operator may manually retrieve stored pictures from the DKL database and compare the cases of similar classification to the wafer picture received from the inspection tool. The pixel classification can then be manually compared to previously stored wafer patterns, for example looking for similar ‘footprint’ patterns. The Operator may then determine that particular wafers exhibit similar pixel patterns to previously stored patterns, and therefore may have the same, or similar, defect.
Hence, an Operator uses manual data analysis and manual picture retrieval technology to match current wafer fabrication (FAB) defect problems with historical defect cases accumulated through the Defect Knowledge Library (DKL).
This human visual inspection process is renowned for being inaccurate due to various factors including stress, eye fatigue and boredom of the Operator. Furthermore, it is prone to human judgment and therefore prone to the inconsistencies due to different perceptions by different Operators as to the significance of a finding.
In addition, the above inspection approach has the disadvantage that the process is very time consuming. In this regard, there is a lengthy delay before a particular wafer-manufacturing problem is identified, and steps taken to rectify it.
Finally, the human visual inspection process is limited to a set of pre-defined classifications, which may well not encompass the wafer manufacturing problem that resulted in the wafer defect type encountered.
U.S. Pat. No. 5,129,009 describes a method for inspecting integrated circuits (ICs) and performing direction edge enhancements based on a comparison between first and second edge enhanced images. Defects in images, once located, are classified and combined to form a feature matrix. The feature matrix is then compared to an expert system database having a large number of features matrices associated with defect classifications.
Thus, there exists a need in the field of the present invention to provide an improved method and apparatus for determining a response to a wafer manufacturing process problem, wherein the abovementioned disadvantages may be alleviated.